Therapeutic Radiology

Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model.

Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose selection is often guided by experience rather than individual patient and tumor specific features. A data-driven approach that links dose to quantitative local control and adverse effect predictions could improve planning and follow-up strategies. We p...

Machine learning for survival prediction after brain tumor stereotactic radiosurgery: A systematic review and meta-analysis.

Explicit modeling of beam geometry improves three-dimensional dose prediction for esophageal cancer radiotherapy under heterogeneous beam configurations.

PURPOSE: Deep learning-based 3D dose prediction boosts radiotherapy planning efficiency and consistency, yet most models rely solely on anatomical dat...

Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.

BACKGROUND: Deep learning methods have made great progress in the automatic segmentation of nasopharyngeal carcinoma, but challenges remain. PURPOSE: ...

Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.

High-quality radiotherapy dose distribution prediction for linear accelerators remains a labor-intensive process constrained by inter-planner variabil...

A review of clinical trial radiotherapy quality assurance: value, evolution and lessons.

BACKGROUND AND PURPOSE: Deviations in radiotherapy quality can significantly affect clinical trial outcomes, including overall survival. Radiotherapy ...

MRI-Guided High-Intensity Focused Ultrasound in Movement Disorders: Targeting Pathologic Brain Circuits With Precision Imaging, From the AJR Special Series on Critical Anatomy.

MRI-guided high-intensity focused ultrasound (MRgHIFU) has emerged as an alternative to other neuromodulatory interventions for patients with medicall...

Unsupervised clustering analysis unravels the role of systemic inflammatory indices in the prognosis of patients with locally advanced cervical cancer treated with chemoradiation.

BACKGROUND AND PURPOSE: This study aimed to predict the treatment outcomes and survival of patients with locally advanced cervical cancer (LACC) recei...

Functional-based multi-omics early prediction of radiation pneumonitis in NSCLC using AI-generated perfusion and ventilation from planning CT.

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and...

Transformer-encoded nnU-Net with local region perceptron and contrastive learning (TLC-nnUNet) for multiple brain metastasis detection and delineation.

Accurate detection and segmentation of multiple brain metastases (BMs) on MRI remain challenging, particularly for those involving small lesions (long...

Multi-reader evaluation of deep learning-based auto-segmentation of eloquent brain arteriovenous malformation on MRA and white matter tractography in stereotactic radiosurgery.

OBJECTIVE: To minimize the radiation injury for white matter (WM) pathways during brain arteriovenous malformation (bAVM) stereotactic radiosurgery (S...

From data to treatment plan: An AI-driven path for automated breast radiotherapy planning.

BACKGROUND: Breast cancer is one of the most prevalent malignancies in women, with radiotherapy (RT) playing a key role in its treatment. Advances in ...

Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.

PURPOSE: Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatme...

Individualized gamma knife radiosurgery prescription dosing for pituitary adenomas: development and internal validation of a feedforward neural network model.

BACKGROUND: Gamma Knife radiosurgery (GKRS) is an established treatment for pituitary adenomas yet prescription dose selection is often guided by clin...

Artificial intelligence improves survival prediction in patients with brain metastases submitted to radiosurgery.

Gamma knife radiosurgery for cerebellar brain metastases: clinical outcomes and artificial intelligence-based predictive modeling.

Evaluating dose distribution in prostate IMRT patients using deep learning: the influence of loss function on model performance.

To estimate the influence of various loss functions on the performance of deep learning (DL) models for dose prediction in intensity-modulated radioth...

EXPRESS: Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. T...

Advances in radiomics for predicting and managing xerostomia following radiotherapy: A systematic review.

BACKGROUND: Radiomics has emerged as a promising approach for predicting radiotherapy (RT)- induced xerostomia in head and neck cancer (HNC) patients,...

Slice-prompted HR-CTV interactive segmentation for cervical cancer brachytherapy: A multi-center study.

BACKGROUND: In computed tomography (CT)-guided cervical cancer brachytherapy, the manual contouring for the high-risk clinical target volume (HR-CTV) ...